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import torch |
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from torch import Tensor |
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from torch import nn |
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from typing import Union, Tuple, List, Iterable, Dict |
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import os |
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import json |
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class WeightedLayerPooling(nn.Module): |
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""" |
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Token embeddings are weighted mean of their different hidden layer representations |
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""" |
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def __init__(self, word_embedding_dimension, num_hidden_layers: int = 12, layer_start: int = 4, layer_weights = None): |
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super(WeightedLayerPooling, self).__init__() |
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self.config_keys = ['word_embedding_dimension', 'layer_start', 'num_hidden_layers'] |
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self.word_embedding_dimension = word_embedding_dimension |
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self.layer_start = layer_start |
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self.num_hidden_layers = num_hidden_layers |
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self.layer_weights = layer_weights if layer_weights is not None else nn.Parameter(torch.tensor([1] * (num_hidden_layers+1 - layer_start), dtype=torch.float)) |
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def forward(self, features: Dict[str, Tensor]): |
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ft_all_layers = features['all_layer_embeddings'] |
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all_layer_embedding = torch.stack(ft_all_layers) |
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all_layer_embedding = all_layer_embedding[self.layer_start:, :, :, :] |
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weight_factor = self.layer_weights.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).expand(all_layer_embedding.size()) |
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weighted_average = (weight_factor*all_layer_embedding).sum(dim=0) / self.layer_weights.sum() |
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features.update({'token_embeddings': weighted_average}) |
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return features |
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def get_word_embedding_dimension(self): |
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return self.word_embedding_dimension |
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def get_config_dict(self): |
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return {key: self.__dict__[key] for key in self.config_keys} |
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def save(self, output_path): |
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with open(os.path.join(output_path, 'config.json'), 'w') as fOut: |
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json.dump(self.get_config_dict(), fOut, indent=2) |
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torch.save(self.state_dict(), os.path.join(output_path, 'pytorch_model.bin')) |
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@staticmethod |
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def load(input_path): |
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with open(os.path.join(input_path, 'config.json')) as fIn: |
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config = json.load(fIn) |
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model = WeightedLayerPooling(**config) |
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model.load_state_dict(torch.load(os.path.join(input_path, 'pytorch_model.bin'), map_location=torch.device('cpu'))) |
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return model |
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